How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok

• URL: http://arxiv.org/abs/2206.04720v2
• Date: Wed, 13 Jul 2022 17:46:46 GMT
• Title: How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok
• Authors: Orestis Papakyriakopoulos, Christelle Tessono, Arvind Narayanan, Mihir Kshirsagar
• Abstract summary: We analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.
• Score: 5.851101657703105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online platforms play an increasingly important role in shaping democracy by influencing the distribution of political information to the electorate. In recent years, political campaigns have spent heavily on the platforms' algorithmic tools to target voters with online advertising. While the public interest in understanding how platforms perform the task of shaping the political discourse has never been higher, the efforts of the major platforms to make the necessary disclosures to understand their practices falls woefully short. In this study, we collect and analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.

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